
 TSEMO log file created on 10-Dec-2022 

 This file shows the initial specifications of TSEMO and logs the output.
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 License information 

 BSD 2-Clause License 

 Copyright (c) 2017, Eric Bradford, Artur M. Schweidtmann and Alexei Lapkin
 All rights reserved. 

 Redistribution and use in source and binary forms, with or without
 modification, are permitted provided that the following conditions are met: 

 *Redistributions of source code must retain the above copyright notice, this   
  list of conditions and the following disclaimer. 

 *Redistributions in binary form must reproduce the above copyright notice,   
  this list of conditions and the following disclaimer in the documentation   
  and/or other materials provided with the distribution. 

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 Problem specifications 

 Function used:   vlmop2 

 Number of inputs:   2
 Number of outputs:  2 

 Lower bounds of decision variables:
      x1      x2
 -2.0000 -2.0000
 
 Upper bounds of decision variables:
      x1      x2
  2.0000  2.0000
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 Algorithm options 

 Maximum number of function evaluations:  40
 Sample batch size:                       5
 Number of algorithm iterations:          8 

 Genetic algorithm population size:        100
 Genetic algorithm number of generations:  100 

                                               f1      f2
 Number of spectral sampling points:         4000    4000
 Type of matern function:                       1       1
 Direct evaluations per input dimension:      200     200
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 Initial data set 

 Number of initial data points:  10 

 Initial input data matrix:
      x1      x2
  0.1351 -1.1091
 -0.9864 -0.5773
 -0.6034  1.4086
 -1.3397  0.3001
  1.1230  0.4946
  1.9572 -1.4466
  0.7363  0.9452
 -1.9370 -1.6260
 -0.0575  1.9763
  1.5067 -0.2680

 Initial output data matrix:
      f1      f2
  0.9734  0.5814
  0.9891  0.0905
  0.8903  0.9887
  0.9872  0.7570
  0.1960  0.9917
  0.9980  0.9995
  0.0559  0.9919
  1.0000  0.9053
  0.8887  0.9995
  0.7961  0.9939

 ¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
 Algorithm iteration 1 

 Predicted hypervolume improvement:    8.9152
 Time taken:    6.6908 

 Proposed evaluation point(s): 
      x1      x2
  0.3510 -0.4846
  0.4119  1.3263
  0.2356 -0.5036
  0.0066 -0.5112
  0.9548  1.9977

 Corresponding observation(s): 
      f1      f2
  0.7871  0.3753
  0.8151  0.8612
  0.8222  0.6894
  0.9954  0.6055
  0.4217  1.0000

 Current hyperparameter values: 
  Hyperparameter      f1      f2
         lambda1  0.0788  0.0638
         lambda2  0.0743  0.0506
          sigmaf  0.2788  0.2616
          sigman  0.0007  0.0007

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 Algorithm iteration 2 

 Predicted hypervolume improvement:    6.3141
 Time taken:    6.9115 

 Proposed evaluation point(s): 
      x1      x2
  1.6605  0.9029
 -0.1659 -0.6249
  1.8508  0.9017
  1.6661  1.1776
 -0.5936 -0.6459

 Corresponding observation(s): 
      f1      f2
  0.6122  0.9208
  0.7397  0.6805
  0.9705  0.9997
  0.2589  0.9999
  0.9999  0.0165

 Current hyperparameter values: 
  Hyperparameter      f1      f2
         lambda1  0.1695  0.1462
         lambda2  0.0946  0.0657
          sigmaf  0.2808  0.3243
          sigman  0.0007  0.0008

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 Algorithm iteration 3 

 Predicted hypervolume improvement:    4.2806
 Time taken:    6.4061 

 Proposed evaluation point(s): 
      x1      x2
  1.2032  0.9983
 -1.1331 -0.5609
  1.0809 -0.5331
  1.1256  0.9999
 -1.2465 -0.6331

 Corresponding observation(s): 
      f1      f2
  0.2817  0.9932
  0.8132  0.2296
  0.9963  0.9986
  0.1836  0.9603
  0.9981  0.2565

 Current hyperparameter values: 
  Hyperparameter      f1      f2
         lambda1  0.1225  0.2217
         lambda2  0.1089  0.0825
          sigmaf  0.3055  0.3050
          sigman  0.0007  0.0008

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 Algorithm iteration 4 

 Predicted hypervolume improvement:    3.8663
 Time taken:    6.5084 

 Proposed evaluation point(s): 
      x1      x2
  0.1628  0.0942
 -0.5248 -0.6096
  0.5380  0.6987
 -0.5209  0.1035
  0.1553  0.5026

 Corresponding observation(s): 
      f1      f2
  0.4892  0.9613
  0.0283  0.8462
  0.2927  0.7531
  0.0419  0.9706
  0.4993  0.8900

 Current hyperparameter values: 
  Hyperparameter      f1      f2
         lambda1  0.1913  0.2353
         lambda2  0.1602  0.1192
          sigmaf  0.3283  0.2941
          sigman  0.0008  0.0009

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 Algorithm iteration 5 

 Predicted hypervolume improvement:    2.4228
 Time taken:    5.9335 

 Proposed evaluation point(s): 
      x1      x2
 -0.4544 -0.7539
  0.6576  0.5268
 -1.1752 -1.2188
  0.6706  0.7563
  0.6684  0.2256

 Corresponding observation(s): 
      f1      f2
  0.9693  0.0344
  0.9993  0.0037
  0.2081  0.0639
  0.9661  0.3818
  0.9824  0.9368

 Current hyperparameter values: 
  Hyperparameter      f1      f2
         lambda1  0.2801  0.2667
         lambda2  0.2338  0.1801
          sigmaf  0.3900  0.3088
          sigman  0.0009  0.0009

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 Algorithm iteration 6 

 Predicted hypervolume improvement:    2.0365
 Time taken:    6.7643 

 Proposed evaluation point(s): 
      x1      x2
 -0.0935 -0.7806
  0.9065  0.7189
  0.4584 -0.7490
 -0.6394 -0.7450
  0.7136  0.7011

 Corresponding observation(s): 
      f1      f2
  0.9424  0.0391
  0.8872  0.9802
  0.0001  0.3174
  0.9903  0.7434
  0.0060  0.9817

 Current hyperparameter values: 
  Hyperparameter      f1      f2
         lambda1  0.3768  0.2914
         lambda2  0.3405  0.2569
          sigmaf  0.4025  0.3198
          sigman  0.0009  0.0009

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 Algorithm iteration 7 

 Predicted hypervolume improvement:    1.2661
 Time taken:    6.4121 

 Proposed evaluation point(s): 
      x1      x2
 -0.3793 -0.0175
 -0.4124 -0.4529
  0.1321 -0.0174
  0.1415  0.5710
 -0.4146 -0.6019

 Corresponding observation(s): 
      f1      f2
  0.8183  0.9256
  0.5749  0.2871
  0.9488  0.4418
  0.1406  0.6927
  0.9050  0.0921

 Current hyperparameter values: 
  Hyperparameter      f1      f2
         lambda1  0.4585  0.3418
         lambda2  0.4179  0.3244
          sigmaf  0.3845  0.3275
          sigman  0.0009  0.0009

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 Algorithm iteration 8 

 Predicted hypervolume improvement:    1.4894
 Time taken:    6.5802 

 Proposed evaluation point(s): 
      x1      x2
 -0.2662 -0.1710
  0.3048  0.3694
 -0.4243 -0.3615
  0.3017 -0.1621
  0.7341  0.3723

 Corresponding observation(s): 
      f1      f2
  0.8207  0.2411
  0.9113  0.6014
  0.1067  0.3823
  0.8873  0.1808
  0.7315  0.9609

 Current hyperparameter values: 
  Hyperparameter      f1      f2
         lambda1  0.5051  0.4181
         lambda2  0.4690  0.4222
          sigmaf  0.3909  0.3371
          sigman  0.0009  0.0009

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 Final algorithm output 

 Final input data matrix:
      x1      x2
  0.1351 -1.1091
 -0.9864 -0.5773
 -0.6034  1.4086
 -1.3397  0.3001
  1.1230  0.4946
  1.9572 -1.4466
  0.7363  0.9452
 -1.9370 -1.6260
 -0.0575  1.9763
  1.5067 -0.2680
  0.3510 -0.4846
  0.4119  1.3263
  0.2356 -0.5036
  0.0066 -0.5112
  0.9548  1.9977
  1.6605  0.9029
 -0.1659 -0.6249
  1.8508  0.9017
  1.6661  1.1776
 -0.5936 -0.6459
  1.2032  0.9983
 -1.1331 -0.5609
  1.0809 -0.5331
  1.1256  0.9999
 -1.2465 -0.6331
  0.1628  0.0942
 -0.5248 -0.6096
  0.5380  0.6987
 -0.5209  0.1035
  0.1553  0.5026
 -0.4544 -0.7539
  0.6576  0.5268
 -1.1752 -1.2188
  0.6706  0.7563
  0.6684  0.2256
 -0.0935 -0.7806
  0.9065  0.7189
  0.4584 -0.7490
 -0.6394 -0.7450
  0.7136  0.7011
 -0.3793 -0.0175
 -0.4124 -0.4529
  0.1321 -0.0174
  0.1415  0.5710
 -0.4146 -0.6019
 -0.2662 -0.1710
  0.3048  0.3694
 -0.4243 -0.3615
  0.3017 -0.1621
  0.7341  0.3723

 Final output data matrix:
      f1      f2
  0.9734  0.5814
  0.9891  0.0905
  0.8903  0.9887
  0.9872  0.7570
  0.1960  0.9917
  0.9980  0.9995
  0.0559  0.9919
  1.0000  0.9053
  0.8887  0.9995
  0.7961  0.9939
  0.7871  0.6894
  0.3753  0.9954
  0.8151  0.6055
  0.8612  0.4217
  0.8222  1.0000
  0.6122  0.9997
  0.9208  0.2589
  0.7397  0.9999
  0.6805  0.9999
  0.9705  0.0165
  0.2817  0.9986
  0.9932  0.1836
  0.8132  0.9603
  0.2296  0.9981
  0.9963  0.2565
  0.4892  0.7531
  0.9613  0.0419
  0.0283  0.9706
  0.8462  0.4993
  0.2927  0.8900
  0.9693  0.0639
  0.0344  0.9661
  0.9993  0.3818
  0.0037  0.9824
  0.2081  0.9368
  0.9424  0.3174
  0.0391  0.9903
  0.8872  0.7434
  0.9802  0.0060
  0.0001  0.9817
  0.8183  0.4418
  0.9256  0.1406
  0.5749  0.6927
  0.2871  0.9050
  0.9488  0.0921
  0.8207  0.3823
  0.2411  0.8873
  0.9113  0.1808
  0.6014  0.7315
  0.1067  0.9609

 Input data matrix of corresponding Pareto front:
      x1      x2
  0.3510 -0.4846
  0.2356 -0.5036
 -0.5936 -0.6459
  0.1628  0.0942
 -0.5248 -0.6096
  0.5380  0.6987
  0.6576  0.5268
  0.6684  0.2256
 -0.6394 -0.7450
  0.7136  0.7011
 -0.3793 -0.0175
 -0.4124 -0.4529
  0.1321 -0.0174
 -0.4146 -0.6019
 -0.2662 -0.1710
  0.3048  0.3694
 -0.4243 -0.3615
  0.7341  0.3723

 Output data matrix of corresponding Pareto front:
      f1      f2
  0.7871  0.6894
  0.8151  0.6055
  0.9705  0.0165
  0.4892  0.7531
  0.9613  0.0419
  0.0283  0.9706
  0.0344  0.9661
  0.2081  0.9368
  0.9802  0.0060
  0.0001  0.9817
  0.8183  0.4418
  0.9256  0.1406
  0.5749  0.6927
  0.9488  0.0921
  0.8207  0.3823
  0.2411  0.8873
  0.9113  0.1808
  0.1067  0.9609

 Input data matrix of Pareto front of final Gaussian process model:
      x1      x2
 -0.6413 -0.7630
  0.6179  0.7100
 -0.4611 -0.6370
 -0.0740  0.1495
 -0.0246  0.1520
 -0.3934 -0.6232
 -0.6408 -0.6281
 -0.6445 -0.3419
 -0.0153  0.2829
  0.4361  0.3757
 -0.4468 -0.6331
  0.4381  0.5034
 -0.3264 -0.3408
  0.4461  0.3471
  0.0634 -0.0623
 -0.4042 -0.6305
 -0.0244  0.2832
 -0.3145 -0.4050
 -0.0189  0.1847
 -0.0124  0.3808
 -0.0901  0.1558
 -0.0783 -0.0662
  0.1847  0.1498
  0.1770  0.1882
  0.1688  0.2839
  0.1068  0.2857
  0.4441  0.1890
 -0.0994  0.1580
  0.1760  0.2899
 -0.0757 -0.0582
  0.5465  0.5074
  0.4724  0.3804
 -0.2066 -0.2665
 -0.6411 -0.6376
  0.5170  0.3766
 -0.0765  0.2887
 -0.0748 -0.3824
 -0.0252 -0.2617
 -0.0786 -0.2469
 -0.0247  0.0159
  0.2444  0.2846
 -0.1398 -0.2565
 -0.3924 -0.3424
 -0.0291 -0.0625
 -0.0772 -0.0910
  0.5175  0.2801
 -0.0012 -0.0525
  0.5889  0.7129
  0.5665  0.3792
 -0.0833 -0.1892
 -0.3870 -0.3392
  0.6127  0.7855
  0.5742  0.5398
 -0.4302 -0.4392
  0.5351  0.3803
  0.2803  0.2817
 -0.0882 -0.1004
 -0.0852  0.2861
  0.4936  0.5311
 -0.4498 -0.4111
  0.4409  0.2527
 -0.1937 -0.3043
  0.6000  0.6946
 -0.3144 -0.2688
 -0.3829 -0.4071
 -0.2557 -0.2531
 -0.4411 -0.3474
 -0.0802 -0.1307
  0.6124  0.5660
 -0.1019 -0.0694
 -0.0828 -0.2843
 -0.3261 -0.3846
  0.4326  0.3246
 -0.4466 -0.3971
 -0.1067 -0.3850
 -0.3891 -0.4094
  0.6000  0.5017
 -0.1103 -0.2570
 -0.3272 -0.2915
  0.4485  0.2918
  0.5234  0.5288
 -0.0950 -0.0674
 -0.0082 -0.0647
 -0.3386 -0.2951
 -0.0981 -0.2624
  0.4207  0.3158
 -0.0229 -0.0571
 -0.0639 -0.1251
  0.6256  0.7215
  0.4128  0.2742
 -0.0800 -0.2675
  0.5608  0.4030
 -0.0680  0.2763
 -0.0755 -0.2851
 -0.1450 -0.2840
  0.4702  0.3800
 -0.1117 -0.0990
 -0.1455 -0.2888
 -0.2610 -0.2575
  0.5571  0.4047

 Output data matrix of Pareto front of final Gaussian process model:
      f1      f2
  0.9732 -0.0193
 -0.0475  0.9862
  0.9433  0.0412
  0.5160  0.6427
  0.5002  0.6604
  0.9145  0.0948
  0.9439 -0.0051
  0.9103  0.1320
  0.4298  0.7260
  0.2088  0.8645
  0.9252  0.0499
  0.1564  0.8948
  0.8386  0.2720
  0.2472  0.8644
  0.5799  0.6227
  0.9204  0.0774
  0.4442  0.7157
  0.8547  0.2464
  0.4856  0.6794
  0.3936  0.7772
  0.5404  0.6318
  0.6209  0.5252
  0.4198  0.7515
  0.4057  0.7542
  0.3514  0.7860
  0.3806  0.7821
  0.3423  0.8237
  0.5512  0.6250
  0.3424  0.7958
  0.6073  0.5414
  0.0520  0.9424
  0.1848  0.8881
  0.7979  0.3700
  0.9496 -0.0148
  0.1424  0.9044
  0.4621  0.7028
  0.7823  0.3760
  0.7097  0.4636
  0.7179  0.4487
  0.5910  0.6086
  0.3241  0.8279
  0.7595  0.4002
  0.8683  0.2109
  0.6063  0.5572
  0.6402  0.5217
  0.2053  0.8826
  0.5915  0.5940
 -0.0249  0.9797
  0.0954  0.9201
  0.6988  0.4710
  0.8640  0.2240
 -0.0093  0.9614
  0.0415  0.9481
  0.9075  0.1497
  0.1295  0.9110
  0.3141  0.8355
  0.6654  0.4993
  0.4767  0.6911
  0.0837  0.9273
  0.8940  0.1521
  0.2978  0.8373
  0.8105  0.3603
 -0.0165  0.9696
  0.8216  0.3157
  0.8720  0.1934
  0.8170  0.3353
  0.8843  0.1740
  0.6907  0.4817
  0.0088  0.9582
  0.6575  0.5089
  0.7366  0.4232
  0.8572  0.2366
  0.2619  0.8602
  0.8877  0.1647
  0.8153  0.3532
  0.8776  0.1873
  0.0275  0.9543
  0.7498  0.4071
  0.8243  0.2965
  0.2639  0.8493
  0.0783  0.9389
  0.6480  0.5140
  0.5923  0.5723
  0.8345  0.2845
  0.7408  0.4126
  0.2753  0.8431
  0.5959  0.5686
  0.6794  0.4955
 -0.0440  0.9800
  0.2849  0.8410
  0.7223  0.4353
  0.1130  0.9188
  0.4714  0.7002
  0.7257  0.4295
  0.7775  0.3957
  0.1903  0.8839
  0.6874  0.4922
  0.7788  0.3932
  0.8175  0.3228
  0.1157  0.9137

 Final hyperparameter values: 
  Hyperparameter      f1      f2
         lambda1  9.3407  7.4540
         lambda2  8.8402  7.8233
          sigmaf  0.4018  0.3454
          sigman  0.0009  0.0009
